PhD Thesis Poppinga: RRT - Jacobs University

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3D Sensing (a) Close boxes (b) Dark material (c) Sun Figure 2.18: WEBCAM IMAGES FOR SCENES DEMONSTRATING THE CORRECTION OF ERRORS OTHER THAN WRAP-AROUND – The webcam is mounted directly next to the SwissRanger. The SwissRanger images can be found in figure 2.19. Table 2.4: Results on all six scenes Scene Discarded Pixels [%] Proposed method AT 160 AT 240 Rack and door 16.7 46.3 60.3 Corner window 21.7 43.3 54.7 Gates 56.3 92.8 100 Close boxes 38.1 35.0 – Dark material 13.1 28.0 43.1 Sun 44.9 73.1 84.6 mean 31.8 53.1 68.5 median 44.8 29.9 60.3 46
2.5 Case study: The SwissRanger time-of-flight camera (a) Unfiltered distance image: (b) No wrap-around, but noisy Dark obstacles in the back appear too close dark and too close trouser due to material (c) Sun causes random noise (d) Unfiltered distance image with objects removed, for comparison (e) Proposed method: dark pixels removed – 13.1 % d. p. (f) Proposed method: very little noise remains – 44.9 % d. p. (g) Proposed method: correct pixels of both back- and foreground kept – 38.1 % d. p. (h) AT 160: dark objects discarded, on edges more than in center – 28.0 % d. p. (i) AT 160: no noise, but parts of floor and locker removed – 73.1 % d. p. (j) AT 160: pixels in the front preserved, those in the back gone – 35.0 % d. p. (k) AT 240 – 43.1 % d. p. (l) AT 240 – 84.6 % d. p. Figure 2.19: SWISSRANGER IMAGES FOR SCENES DEMONSTRATING THE CORRECTION OF ER- RORS OTHER THAN WRAP-AROUND – From left column to right: light scattering, too low reflectivity, and sun light. Here, an AT of 160 is sufficient, most AT 240 images are still given for completeness. We also give the percentage of discarded pixels (d.p.). Webcam images of the scenes are in figure 2.18. 47
Page 1: Towards Autonomous Navigation for R
Page 4 and 5: ased on RRT. We compare our approac
Page 6 and 7: • Jann Poppinga, Max Pfingsthorn,
Page 9 and 10: Contents 1 Introduction 19 1.1 Obst
Page 11 and 12: List of Figures 1.1 Rugbot at a dri
Page 13 and 14: LIST OF FIGURES 6.3 The lab model t
Page 15 and 16: List of Tables 2.1 Specification of
Page 17 and 18: List of Algorithms 1 General form o
Page 19 and 20: Chapter 1 Introduction Robots have
Page 21 and 22: 1.2 3D Mapping The major problem of
Page 23 and 24: 1.2 3D Mapping Figure 1.3: An indic
Page 25 and 26: 1.3 3D Path Planning 1.3 3D Path Pl
Page 27 and 28: Chapter 2 3D Sensing Mobile robotic
Page 29 and 30: 2.2 Stereo cameras object r f b f d
Page 31 and 32: 2.4 Sonar (a) Distance image (b) Am
Page 33 and 34: 2.5 Case study: The SwissRanger tim
Page 45: 2.5 Case study: The SwissRanger tim
Page 51 and 52: Chapter 3 Datasets In this chapter,
Page 53 and 54: 3.1 Datasets with range cameras Fig
Page 55 and 56: 3.2 Datasets with actuated LRF (a)
Page 57 and 58: 3.2 Datasets with actuated LRF Figu
Page 59 and 60: 3.3 Dataset with sonar: Lesumsperrw
Page 61 and 62: 3.4 Summary Figure 3.13: An overvie
Page 63 and 64: 3.4 Summary Table 3.2: SPATIAL PROP
Page 65 and 66: Chapter 4 Near Field 3D Navigation
Page 67 and 68: 4.1 Approach and Implementation z U
Page 69 and 70: 4.2 Experiments and results Table 4
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Page 73 and 74: 4.2 Experiments and results 55 50 O
Page 79 and 80: Chapter 5 Patch Map Data-Structure
Page 81 and 82: 5.2 Surface Patches Data set Patch
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5.3 Generation of patches from poin
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Patch Map Data-Structure collision
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Patch Map Data-Structure P 2 P 3 P
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Patch Map Data-Structure (a) Neares
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Patch Map Data-Structure Algorithm
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Patch Map Data-Structure Figure 5.1
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Patch Map Data-Structure path lengt
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112 Patch Map Data-Structure
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3D Roadmaps for Unmanned Aerial Veh
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130 3D Roadmaps for Unmanned Aerial
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Conclusion variant of the map data-
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Addenda & Errata (a) Outline on the
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[Birk et al., 2010] Birk, A., Patha
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[Gottschalk, 1997] Gottschalk, S. (
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[Konolige, 1999] Konolige, K. (1999
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[Mustafa, 2004] Mustafa, N. H. (200
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[Rusu et al., 2008a] Rusu, R. B., M
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[Unnikrishnan and Hebert, 2003] Unn
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PhD Thesis Poppinga: RRT - Jacobs University

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